Overview

Dataset statistics

Number of variables14
Number of observations13019794
Missing cells16469448
Missing cells (%)9.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 GiB
Average record size in memory93.6 B

Variable types

Numeric8
Categorical6

Alerts

fqid has a high cardinality: 127 distinct valuesHigh cardinality
text_fqid has a high cardinality: 126 distinct valuesHigh cardinality
df_index is highly overall correlated with elapsed_time and 1 other fieldsHigh correlation
elapsed_time is highly overall correlated with df_index and 2 other fieldsHigh correlation
level is highly overall correlated with df_index and 3 other fieldsHigh correlation
room_coor_x is highly overall correlated with screen_coor_xHigh correlation
room_coor_y is highly overall correlated with screen_coor_yHigh correlation
screen_coor_x is highly overall correlated with room_coor_xHigh correlation
screen_coor_y is highly overall correlated with room_coor_yHigh correlation
event_name is highly overall correlated with nameHigh correlation
name is highly overall correlated with event_nameHigh correlation
room_fqid is highly overall correlated with level and 1 other fieldsHigh correlation
level_group is highly overall correlated with elapsed_time and 2 other fieldsHigh correlation
name is highly imbalanced (52.6%)Imbalance
room_coor_x has 1022943 (7.9%) missing valuesMissing
room_coor_y has 1022943 (7.9%) missing valuesMissing
screen_coor_x has 1022943 (7.9%) missing valuesMissing
screen_coor_y has 1022943 (7.9%) missing valuesMissing
fqid has 4110692 (31.6%) missing valuesMissing
text_fqid has 8266984 (63.5%) missing valuesMissing
level has 341298 (2.6%) zerosZeros

Reproduction

Analysis started2023-03-27 04:41:34.822017
Analysis finished2023-03-27 04:48:44.095028
Duration7 minutes and 9.27 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

session_id
Real number (ℝ)

Distinct11648
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1146275 × 1016
Minimum2.0090312 × 1016
Maximum2.2100221 × 1016
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size714.7 MiB
2023-03-27T04:48:44.155477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.0090312 × 1016
5-th percentile2.0100518 × 1016
Q12.1010315 × 1016
median2.1040309 × 1016
Q32.1100615 × 1016
95-th percentile2.2070308 × 1016
Maximum2.2100221 × 1016
Range2.0099087 × 1015
Interquartile range (IQR)9.0299871 × 1013

Descriptive statistics

Standard deviation5.5873038 × 1014
Coefficient of variation (CV)0.026422166
Kurtosis-0.11944556
Mean2.1146275 × 1016
Median Absolute Deviation (MAD)4.0000371 × 1013
Skewness0.10874842
Sum2.4888407 × 1018
Variance3.1217964 × 1029
MonotonicityIncreasing
2023-03-27T04:48:44.255987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.011031046 × 101619032
 
0.1%
2.111031343 × 10165435
 
< 0.1%
2.104031342 × 10164248
 
< 0.1%
2.011021926 × 10164215
 
< 0.1%
2.103031358 × 10163900
 
< 0.1%
2.100050936 × 10163768
 
< 0.1%
2.105050856 × 10163618
 
< 0.1%
2.203052013 × 10163538
 
< 0.1%
2.105021439 × 10163484
 
< 0.1%
2.101040917 × 10163411
 
< 0.1%
Other values (11638) 12965145
99.6%
ValueCountFrequency (%)
2.009031243 × 1016881
< 0.1%
2.009031243 × 10161831
< 0.1%
2.009031412 × 10161038
< 0.1%
2.009031436 × 10161066
< 0.1%
2.009031444 × 1016789
< 0.1%
2.009031508 × 10161527
< 0.1%
2.009031509 × 1016953
< 0.1%
2.00903151 × 10161100
< 0.1%
2.009031517 × 1016987
< 0.1%
2.009031708 × 10161354
< 0.1%
ValueCountFrequency (%)
2.210022115 × 10161547
< 0.1%
2.210021944 × 1016868
 
< 0.1%
2.21002171 × 10161199
< 0.1%
2.210021546 × 1016985
< 0.1%
2.210021534 × 1016873
 
< 0.1%
2.210021524 × 10161074
< 0.1%
2.210021519 × 10162299
< 0.1%
2.210021503 × 1016930
< 0.1%
2.210021313 × 1016812
 
< 0.1%
2.210021308 × 10161002
< 0.1%

df_index
Real number (ℝ)

Distinct20348
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean652.63195
Minimum0
Maximum20473
Zeros11610
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size714.7 MiB
2023-03-27T04:48:44.361644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile56
Q1289
median596
Q3898
95-th percentile1418
Maximum20473
Range20473
Interquartile range (IQR)609

Descriptive statistics

Standard deviation628.24793
Coefficient of variation (CV)0.96263741
Kurtosis294.21733
Mean652.63195
Median Absolute Deviation (MAD)305
Skewness12.115533
Sum8.4971336 × 109
Variance394695.46
MonotonicityNot monotonic
2023-03-27T04:48:44.454473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86 11618
 
0.1%
90 11618
 
0.1%
89 11618
 
0.1%
88 11618
 
0.1%
87 11618
 
0.1%
85 11618
 
0.1%
53 11617
 
0.1%
59 11617
 
0.1%
58 11617
 
0.1%
57 11617
 
0.1%
Other values (20338) 12903618
99.1%
ValueCountFrequency (%)
0 11610
0.1%
1 11611
0.1%
2 11611
0.1%
3 11611
0.1%
4 11611
0.1%
5 11611
0.1%
6 11611
0.1%
7 11611
0.1%
8 11611
0.1%
9 11611
0.1%
ValueCountFrequency (%)
20473 1
< 0.1%
20472 1
< 0.1%
20471 1
< 0.1%
20470 1
< 0.1%
20469 1
< 0.1%
20468 1
< 0.1%
20467 1
< 0.1%
20466 1
< 0.1%
20465 1
< 0.1%
20464 1
< 0.1%

elapsed_time
Real number (ℝ)

Distinct2885336
Distinct (%)22.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1250903.2
Minimum0
Maximum3691298
Zeros11650
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size665.0 MiB
2023-03-27T04:48:44.560519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile73222
Q1439288
median1013407
Q31740092
95-th percentile3691298
Maximum3691298
Range3691298
Interquartile range (IQR)1300804

Descriptive statistics

Standard deviation1027559.4
Coefficient of variation (CV)0.82145398
Kurtosis0.23054605
Mean1250903.2
Median Absolute Deviation (MAD)623967.5
Skewness1.0163977
Sum1.6286502 × 1013
Variance1.0558784 × 1012
MonotonicityNot monotonic
2023-03-27T04:48:44.658041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3691298 943651
 
7.2%
0 11650
 
0.1%
1014 65
 
< 0.1%
1016 60
 
< 0.1%
949 47
 
< 0.1%
1032 46
 
< 0.1%
4014 43
 
< 0.1%
982 42
 
< 0.1%
966 42
 
< 0.1%
449 42
 
< 0.1%
Other values (2885326) 12064106
92.7%
ValueCountFrequency (%)
0 11650
0.1%
1 4
 
< 0.1%
2 4
 
< 0.1%
3 3
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
14 3
 
< 0.1%
ValueCountFrequency (%)
3691298 943651
7.2%
3691292 1
 
< 0.1%
3691291 1
 
< 0.1%
3691288 2
 
< 0.1%
3691286 1
 
< 0.1%
3691285 4
 
< 0.1%
3691282 1
 
< 0.1%
3691281 1
 
< 0.1%
3691279 1
 
< 0.1%
3691276 1
 
< 0.1%

event_name
Categorical

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size627.8 MiB
navigate_click
5620922 
person_click
2990826 
cutscene_click
1336604 
object_click
1086375 
object_hover
 
522417
Other values (6)
1462650 

Length

Max length18
Median length14
Mean length13.128375
Min length9

Characters and Unicode

Total characters170928739
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcutscene_click
2nd rowperson_click
3rd rowperson_click
4th rowperson_click
5th rowperson_click

Common Values

ValueCountFrequency (%)
navigate_click 5620922
43.2%
person_click 2990826
23.0%
cutscene_click 1336604
 
10.3%
object_click 1086375
 
8.3%
object_hover 522417
 
4.0%
map_hover 465419
 
3.6%
notification_click 320412
 
2.5%
notebook_click 281110
 
2.2%
map_click 255634
 
2.0%
observation_click 104968
 
0.8%

Length

2023-03-27T04:48:44.775143image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
navigate_click 5620922
43.2%
person_click 2990826
23.0%
cutscene_click 1336604
 
10.3%
object_click 1086375
 
8.3%
object_hover 522417
 
4.0%
map_hover 465419
 
3.6%
notification_click 320412
 
2.5%
notebook_click 281110
 
2.2%
map_click 255634
 
2.0%
observation_click 104968
 
0.8%

Most occurring characters

ValueCountFrequency (%)
c 28666328
16.8%
i 18719084
11.0%
e 14302769
8.4%
_ 12984687
7.6%
a 12388277
 
7.2%
k 12313068
 
7.2%
l 11996851
 
7.0%
n 11010361
 
6.4%
t 9628327
 
5.6%
o 7316651
 
4.3%
Other values (11) 31602336
18.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 157944052
92.4%
Connector Punctuation 12984687
 
7.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 28666328
18.1%
i 18719084
11.9%
e 14302769
9.1%
a 12388277
7.8%
k 12313068
7.8%
l 11996851
7.6%
n 11010361
 
7.0%
t 9628327
 
6.1%
o 7316651
 
4.6%
v 6713726
 
4.3%
Other values (10) 24888610
15.8%
Connector Punctuation
ValueCountFrequency (%)
_ 12984687
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 157944052
92.4%
Common 12984687
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 28666328
18.1%
i 18719084
11.9%
e 14302769
9.1%
a 12388277
7.8%
k 12313068
7.8%
l 11996851
7.6%
n 11010361
 
7.0%
t 9628327
 
6.1%
o 7316651
 
4.6%
v 6713726
 
4.3%
Other values (10) 24888610
15.8%
Common
ValueCountFrequency (%)
_ 12984687
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 170928739
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 28666328
16.8%
i 18719084
11.0%
e 14302769
8.4%
_ 12984687
7.6%
a 12388277
 
7.2%
k 12313068
 
7.2%
l 11996851
 
7.0%
n 11010361
 
6.4%
t 9628327
 
5.6%
o 7316651
 
4.3%
Other values (11) 31602336
18.5%

name
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size627.8 MiB
undefined
6301826 
basic
6250867 
close
 
335053
open
 
116647
prev
 
9669

Length

Max length9
Median length5
Mean length6.9259334
Min length4

Characters and Unicode

Total characters90174226
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbasic
2nd rowbasic
3rd rowbasic
4th rowbasic
5th rowbasic

Common Values

ValueCountFrequency (%)
undefined 6301826
48.4%
basic 6250867
48.0%
close 335053
 
2.6%
open 116647
 
0.9%
prev 9669
 
0.1%
next 5732
 
< 0.1%

Length

2023-03-27T04:48:44.851299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-27T04:48:45.197788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
undefined 6301826
48.4%
basic 6250867
48.0%
close 335053
 
2.6%
open 116647
 
0.9%
prev 9669
 
0.1%
next 5732
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 13070753
14.5%
n 12726031
14.1%
d 12603652
14.0%
i 12552693
13.9%
s 6585920
7.3%
c 6585920
7.3%
u 6301826
7.0%
f 6301826
7.0%
a 6250867
6.9%
b 6250867
6.9%
Other values (7) 943871
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 90174226
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 13070753
14.5%
n 12726031
14.1%
d 12603652
14.0%
i 12552693
13.9%
s 6585920
7.3%
c 6585920
7.3%
u 6301826
7.0%
f 6301826
7.0%
a 6250867
6.9%
b 6250867
6.9%
Other values (7) 943871
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 90174226
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 13070753
14.5%
n 12726031
14.1%
d 12603652
14.0%
i 12552693
13.9%
s 6585920
7.3%
c 6585920
7.3%
u 6301826
7.0%
f 6301826
7.0%
a 6250867
6.9%
b 6250867
6.9%
Other values (7) 943871
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90174226
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 13070753
14.5%
n 12726031
14.1%
d 12603652
14.0%
i 12552693
13.9%
s 6585920
7.3%
c 6585920
7.3%
u 6301826
7.0%
f 6301826
7.0%
a 6250867
6.9%
b 6250867
6.9%
Other values (7) 943871
 
1.0%

level
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.189918
Minimum0
Maximum22
Zeros341298
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size627.7 MiB
2023-03-27T04:48:45.290778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q16
median13
Q318
95-th percentile21
Maximum22
Range22
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.4985169
Coefficient of variation (CV)0.53310589
Kurtosis-1.2506216
Mean12.189918
Median Absolute Deviation (MAD)5
Skewness-0.24908394
Sum1.5871022 × 108
Variance42.230722
MonotonicityNot monotonic
2023-03-27T04:48:45.368693image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
18 1737541
 
13.3%
6 1053029
 
8.1%
21 1043130
 
8.0%
11 961457
 
7.4%
16 641104
 
4.9%
7 630640
 
4.8%
15 604717
 
4.6%
19 600837
 
4.6%
17 596216
 
4.6%
3 568599
 
4.4%
Other values (13) 4582524
35.2%
ValueCountFrequency (%)
0 341298
 
2.6%
1 360067
 
2.8%
2 489893
3.8%
3 568599
4.4%
4 211823
 
1.6%
5 330214
 
2.5%
6 1053029
8.1%
7 630640
4.8%
8 440198
3.4%
9 561590
4.3%
ValueCountFrequency (%)
22 208700
 
1.6%
21 1043130
8.0%
20 558349
 
4.3%
19 600837
 
4.6%
18 1737541
13.3%
17 596216
 
4.6%
16 641104
 
4.9%
15 604717
 
4.6%
14 268977
 
2.1%
13 406459
 
3.1%

room_coor_x
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6894153
Distinct (%)57.5%
Missing1022943
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean-54.926779
Minimum-1992.3546
Maximum1261.7738
Zeros1195
Zeros (%)< 0.1%
Negative6175051
Negative (%)47.4%
Memory size665.0 MiB
2023-03-27T04:48:45.490047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1992.3546
5-th percentile-989.15988
Q1-352.95439
median-11.171875
Q3296.40649
95-th percentile721.86768
Maximum1261.7738
Range3254.1284
Interquartile range (IQR)649.36089

Descriptive statistics

Standard deviation520.17517
Coefficient of variation (CV)-9.4703382
Kurtosis0.41246697
Mean-54.926779
Median Absolute Deviation (MAD)324.3277
Skewness-0.52343357
Sum-6.5894838 × 108
Variance270582.22
MonotonicityNot monotonic
2023-03-27T04:48:45.590403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
426.7255249 3452
 
< 0.1%
427.7255249 3327
 
< 0.1%
425.7255249 3313
 
< 0.1%
424.7255249 3212
 
< 0.1%
423.7255249 3193
 
< 0.1%
418.7255249 3140
 
< 0.1%
420.7255249 3137
 
< 0.1%
-37.3484993 3046
 
< 0.1%
421.7255249 3029
 
< 0.1%
430.7255249 3024
 
< 0.1%
Other values (6894143) 11964978
91.9%
(Missing) 1022943
 
7.9%
ValueCountFrequency (%)
-1992.354614 1
 
< 0.1%
-1991.219604 2
< 0.1%
-1989.889648 1
 
< 0.1%
-1989.14624 1
 
< 0.1%
-1988.949585 3
< 0.1%
-1987.814575 1
 
< 0.1%
-1987.723511 1
 
< 0.1%
-1986.348267 1
 
< 0.1%
-1986.05835 1
 
< 0.1%
-1985.045288 1
 
< 0.1%
ValueCountFrequency (%)
1261.773804 1
< 0.1%
1258.348267 1
< 0.1%
1258.311157 1
< 0.1%
1258.241333 1
< 0.1%
1258.133179 1
< 0.1%
1258.11792 1
< 0.1%
1258.109619 1
< 0.1%
1258.089355 1
< 0.1%
1257.992676 1
< 0.1%
1257.838379 1
< 0.1%

room_coor_y
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct5226778
Distinct (%)43.6%
Missing1022943
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean-116.32325
Minimum-918.15869
Maximum543.61639
Zeros9251
Zeros (%)0.1%
Negative8456600
Negative (%)65.0%
Memory size665.0 MiB
2023-03-27T04:48:45.722702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-918.15869
5-th percentile-560.09106
Q1-212.86992
median-97.788986
Q322.655869
95-th percentile201.3427
Maximum543.61639
Range1461.7751
Interquartile range (IQR)235.52579

Descriptive statistics

Standard deviation218.6066
Coefficient of variation (CV)-1.8793027
Kurtosis0.933909
Mean-116.32325
Median Absolute Deviation (MAD)117.78899
Skewness-0.69756204
Sum-1.3955127 × 109
Variance47788.844
MonotonicityNot monotonic
2023-03-27T04:48:45.814533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-106 10087
 
0.1%
-102 10038
 
0.1%
-101 9871
 
0.1%
-103 9870
 
0.1%
-105 9863
 
0.1%
-104 9813
 
0.1%
-108 9754
 
0.1%
-107 9710
 
0.1%
-110 9610
 
0.1%
-100 9483
 
0.1%
Other values (5226768) 11898752
91.4%
(Missing) 1022943
 
7.9%
ValueCountFrequency (%)
-918.1586914 1
< 0.1%
-918.15625 1
< 0.1%
-918.152771 1
< 0.1%
-918.1477051 1
< 0.1%
-918.1400757 1
< 0.1%
-918.1288452 1
< 0.1%
-918.1120605 1
< 0.1%
-918.0871582 1
< 0.1%
-918.0501709 1
< 0.1%
-917.9952393 1
< 0.1%
ValueCountFrequency (%)
543.616394 1
 
< 0.1%
542.2233887 1
 
< 0.1%
542.217041 1
 
< 0.1%
541.9880371 3
< 0.1%
541.9876099 1
 
< 0.1%
541.9866333 1
 
< 0.1%
540.8175659 1
 
< 0.1%
540.3596802 2
< 0.1%
538.8042603 1
 
< 0.1%
536.7374878 3
< 0.1%

screen_coor_x
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct35322
Distinct (%)0.3%
Missing1022943
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean458.1594
Minimum0
Maximum1916
Zeros2843
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size665.0 MiB
2023-03-27T04:48:45.917881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile54
Q1269
median447
Q3663
95-th percentile834
Maximum1916
Range1916
Interquartile range (IQR)394

Descriptive statistics

Standard deviation247.23869
Coefficient of variation (CV)0.53963467
Kurtosis-0.87644112
Mean458.1594
Median Absolute Deviation (MAD)197
Skewness0.026945196
Sum5.49647 × 109
Variance61126.973
MonotonicityNot monotonic
2023-03-27T04:48:46.017101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
822 20554
 
0.2%
814 20258
 
0.2%
817 20258
 
0.2%
824 20233
 
0.2%
383 20172
 
0.2%
819 20141
 
0.2%
382 20139
 
0.2%
427 20137
 
0.2%
820 20127
 
0.2%
823 20077
 
0.2%
Other values (35312) 11794755
90.6%
(Missing) 1022943
 
7.9%
ValueCountFrequency (%)
0 2843
< 0.1%
0.2352941185 1
 
< 0.1%
0.29086411 1
 
< 0.1%
0.328125 10
 
< 0.1%
0.5316009521 1
 
< 0.1%
0.5410461426 1
 
< 0.1%
0.5621948242 3
 
< 0.1%
0.6666564941 1
 
< 0.1%
0.6666666865 1
 
< 0.1%
0.671875 2
 
< 0.1%
ValueCountFrequency (%)
1916 1
 
< 0.1%
1914 1
 
< 0.1%
1906 1
 
< 0.1%
1905 1
 
< 0.1%
1899 1
 
< 0.1%
1891 1
 
< 0.1%
1890 1
 
< 0.1%
1888 1
 
< 0.1%
1882.671875 1
 
< 0.1%
1881 6
< 0.1%

screen_coor_y
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct56858
Distinct (%)0.5%
Missing1022943
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean385.26434
Minimum0
Maximum1439
Zeros97
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size665.0 MiB
2023-03-27T04:48:46.121650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile159
Q1304
median397
Q3471
95-th percentile593
Maximum1439
Range1439
Interquartile range (IQR)167

Descriptive statistics

Standard deviation129.29286
Coefficient of variation (CV)0.3355952
Kurtosis0.32705846
Mean385.26434
Median Absolute Deviation (MAD)83
Skewness-0.25306448
Sum4.6219589 × 109
Variance16716.645
MonotonicityNot monotonic
2023-03-27T04:48:46.216400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
431 43961
 
0.3%
436 43837
 
0.3%
433 43736
 
0.3%
432 43714
 
0.3%
434 43302
 
0.3%
427 43174
 
0.3%
428 43106
 
0.3%
439 43097
 
0.3%
435 43096
 
0.3%
426 43088
 
0.3%
Other values (56848) 11562740
88.8%
(Missing) 1022943
 
7.9%
ValueCountFrequency (%)
0 97
< 0.1%
0.7330170274 1
 
< 0.1%
1 83
< 0.1%
1.265060425 1
 
< 0.1%
1.358886719 1
 
< 0.1%
1.444442749 1
 
< 0.1%
2 70
< 0.1%
3 88
< 0.1%
4 91
< 0.1%
5 133
< 0.1%
ValueCountFrequency (%)
1439 5
< 0.1%
1438 1
 
< 0.1%
1433 1
 
< 0.1%
1420 2
 
< 0.1%
1419 2
 
< 0.1%
1417 1
 
< 0.1%
1412 1
 
< 0.1%
1411 1
 
< 0.1%
1410 5
< 0.1%
1408 2
 
< 0.1%

fqid
Categorical

HIGH CARDINALITY  MISSING 

Distinct127
Distinct (%)< 0.1%
Missing4110692
Missing (%)31.6%
Memory size640.2 MiB
worker
928904 
archivist
 
556979
gramps
 
554611
wells
 
389766
toentry
 
387549
Other values (122)
6091293 

Length

Max length30
Median length25
Mean length10.252915
Min length2

Characters and Unicode

Total characters91344264
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowintro
2nd rowgramps
3rd rowgramps
4th rowgramps
5th rowgramps

Common Values

ValueCountFrequency (%)
worker 928904
 
7.1%
archivist 556979
 
4.3%
gramps 554611
 
4.3%
wells 389766
 
3.0%
toentry 387549
 
3.0%
confrontation 344847
 
2.6%
crane_ranger 249192
 
1.9%
groupconvo 225243
 
1.7%
flag_girl 222197
 
1.7%
tomap 199670
 
1.5%
Other values (117) 4850144
37.3%
(Missing) 4110692
31.6%

Length

2023-03-27T04:48:46.650135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
worker 928904
 
10.4%
archivist 556979
 
6.3%
gramps 554611
 
6.2%
wells 389766
 
4.4%
toentry 387549
 
4.4%
confrontation 344847
 
3.9%
crane_ranger 249192
 
2.8%
groupconvo 225243
 
2.5%
flag_girl 222197
 
2.5%
tomap 199670
 
2.2%
Other values (117) 4850144
54.4%

Most occurring characters

ValueCountFrequency (%)
r 9146869
 
10.0%
o 7413069
 
8.1%
e 7384098
 
8.1%
a 6980261
 
7.6%
t 6556500
 
7.2%
s 6229020
 
6.8%
c 5382230
 
5.9%
n 5307682
 
5.8%
i 4785422
 
5.2%
l 4053928
 
4.4%
Other values (22) 28105185
30.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 85897156
94.0%
Other Punctuation 2353666
 
2.6%
Connector Punctuation 2162572
 
2.4%
Decimal Number 930870
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 9146869
10.6%
o 7413069
 
8.6%
e 7384098
 
8.6%
a 6980261
 
8.1%
t 6556500
 
7.6%
s 6229020
 
7.3%
c 5382230
 
6.3%
n 5307682
 
6.2%
i 4785422
 
5.6%
l 4053928
 
4.7%
Other values (15) 22658077
26.4%
Decimal Number
ValueCountFrequency (%)
0 281013
30.2%
2 270809
29.1%
1 244056
26.2%
3 105880
 
11.4%
4 29112
 
3.1%
Other Punctuation
ValueCountFrequency (%)
. 2353666
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2162572
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 85897156
94.0%
Common 5447108
 
6.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 9146869
10.6%
o 7413069
 
8.6%
e 7384098
 
8.6%
a 6980261
 
8.1%
t 6556500
 
7.6%
s 6229020
 
7.3%
c 5382230
 
6.3%
n 5307682
 
6.2%
i 4785422
 
5.6%
l 4053928
 
4.7%
Other values (15) 22658077
26.4%
Common
ValueCountFrequency (%)
. 2353666
43.2%
_ 2162572
39.7%
0 281013
 
5.2%
2 270809
 
5.0%
1 244056
 
4.5%
3 105880
 
1.9%
4 29112
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 91344264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 9146869
 
10.0%
o 7413069
 
8.1%
e 7384098
 
8.1%
a 6980261
 
7.6%
t 6556500
 
7.2%
s 6229020
 
6.8%
c 5382230
 
5.9%
n 5307682
 
5.8%
i 4785422
 
5.2%
l 4053928
 
4.4%
Other values (22) 28105185
30.8%

room_fqid
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size627.8 MiB
tunic.historicalsociety.entry
1794866 
tunic.wildlife.center
1493791 
tunic.historicalsociety.cage
1164742 
tunic.library.frontdesk
1062749 
tunic.historicalsociety.frontdesk
961191 
Other values (14)
6542455 

Length

Max length39
Median length33
Mean length27.71186
Min length20

Characters and Unicode

Total characters360802705
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtunic.historicalsociety.closet
2nd rowtunic.historicalsociety.closet
3rd rowtunic.historicalsociety.closet
4th rowtunic.historicalsociety.closet
5th rowtunic.historicalsociety.closet

Common Values

ValueCountFrequency (%)
tunic.historicalsociety.entry 1794866
13.8%
tunic.wildlife.center 1493791
11.5%
tunic.historicalsociety.cage 1164742
 
8.9%
tunic.library.frontdesk 1062749
 
8.2%
tunic.historicalsociety.frontdesk 961191
 
7.4%
tunic.historicalsociety.stacks 890014
 
6.8%
tunic.historicalsociety.closet_dirty 778779
 
6.0%
tunic.humanecology.frontdesk 654489
 
5.0%
tunic.historicalsociety.basement 578264
 
4.4%
tunic.kohlcenter.halloffame 540825
 
4.2%
Other values (9) 3100084
23.8%

Length

2023-03-27T04:48:46.741876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tunic.historicalsociety.entry 1794866
13.8%
tunic.wildlife.center 1493791
11.5%
tunic.historicalsociety.cage 1164742
 
8.9%
tunic.library.frontdesk 1062749
 
8.2%
tunic.historicalsociety.frontdesk 961191
 
7.4%
tunic.historicalsociety.stacks 890014
 
6.8%
tunic.historicalsociety.closet_dirty 778779
 
6.0%
tunic.humanecology.frontdesk 654489
 
5.0%
tunic.historicalsociety.basement 578264
 
4.4%
tunic.kohlcenter.halloffame 540825
 
4.2%
Other values (9) 3100084
23.8%

Most occurring characters

ValueCountFrequency (%)
i 42480597
11.8%
t 39743333
11.0%
c 37000946
10.3%
. 26039588
 
7.2%
e 25519252
 
7.1%
o 24226945
 
6.7%
n 22842722
 
6.3%
s 21719911
 
6.0%
r 20047914
 
5.6%
l 19594151
 
5.4%
Other values (16) 81587346
22.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 332584766
92.2%
Other Punctuation 26039588
 
7.2%
Connector Punctuation 1590787
 
0.4%
Decimal Number 587564
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 42480597
12.8%
t 39743333
11.9%
c 37000946
11.1%
e 25519252
 
7.7%
o 24226945
 
7.3%
n 22842722
 
6.9%
s 21719911
 
6.5%
r 20047914
 
6.0%
l 19594151
 
5.9%
a 15511856
 
4.7%
Other values (11) 63897139
19.2%
Decimal Number
ValueCountFrequency (%)
1 221218
37.7%
0 206550
35.2%
2 159796
27.2%
Other Punctuation
ValueCountFrequency (%)
. 26039588
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1590787
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 332584766
92.2%
Common 28217939
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 42480597
12.8%
t 39743333
11.9%
c 37000946
11.1%
e 25519252
 
7.7%
o 24226945
 
7.3%
n 22842722
 
6.9%
s 21719911
 
6.5%
r 20047914
 
6.0%
l 19594151
 
5.9%
a 15511856
 
4.7%
Other values (11) 63897139
19.2%
Common
ValueCountFrequency (%)
. 26039588
92.3%
_ 1590787
 
5.6%
1 221218
 
0.8%
0 206550
 
0.7%
2 159796
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 360802705
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 42480597
11.8%
t 39743333
11.0%
c 37000946
10.3%
. 26039588
 
7.2%
e 25519252
 
7.1%
o 24226945
 
6.7%
n 22842722
 
6.3%
s 21719911
 
6.0%
r 20047914
 
5.6%
l 19594151
 
5.4%
Other values (16) 81587346
22.6%

text_fqid
Categorical

HIGH CARDINALITY  MISSING 

Distinct126
Distinct (%)< 0.1%
Missing8266984
Missing (%)63.5%
Memory size627.8 MiB
tunic.historicalsociety.cage.confrontation
 
329669
tunic.wildlife.center.crane_ranger.crane
 
232811
tunic.historicalsociety.frontdesk.archivist.newspaper
 
212239
tunic.historicalsociety.entry.groupconvo
 
211123
tunic.wildlife.center.wells.nodeer
 
197738
Other values (121)
3569230 

Length

Max length71
Median length54
Mean length43.5566
Min length28

Characters and Unicode

Total characters207016244
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtunic.historicalsociety.closet.intro
2nd rowtunic.historicalsociety.closet.gramps.intro_0_cs_0
3rd rowtunic.historicalsociety.closet.gramps.intro_0_cs_0
4th rowtunic.historicalsociety.closet.gramps.intro_0_cs_0
5th rowtunic.historicalsociety.closet.gramps.intro_0_cs_0

Common Values

ValueCountFrequency (%)
tunic.historicalsociety.cage.confrontation 329669
 
2.5%
tunic.wildlife.center.crane_ranger.crane 232811
 
1.8%
tunic.historicalsociety.frontdesk.archivist.newspaper 212239
 
1.6%
tunic.historicalsociety.entry.groupconvo 211123
 
1.6%
tunic.wildlife.center.wells.nodeer 197738
 
1.5%
tunic.historicalsociety.frontdesk.archivist.have_glass 195039
 
1.5%
tunic.drycleaner.frontdesk.worker.hub 179271
 
1.4%
tunic.historicalsociety.closet_dirty.gramps.news 165221
 
1.3%
tunic.humanecology.frontdesk.worker.intro 148178
 
1.1%
tunic.historicalsociety.frontdesk.archivist_glasses.confrontation 130152
 
1.0%
Other values (116) 2751369
 
21.1%
(Missing) 8266984
63.5%

Length

2023-03-27T04:48:46.835719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tunic.historicalsociety.cage.confrontation 329669
 
6.9%
tunic.wildlife.center.crane_ranger.crane 232811
 
4.9%
tunic.historicalsociety.frontdesk.archivist.newspaper 212239
 
4.5%
tunic.historicalsociety.entry.groupconvo 211123
 
4.4%
tunic.wildlife.center.wells.nodeer 197738
 
4.2%
tunic.historicalsociety.frontdesk.archivist.have_glass 195039
 
4.1%
tunic.drycleaner.frontdesk.worker.hub 179271
 
3.8%
tunic.historicalsociety.closet_dirty.gramps.news 165221
 
3.5%
tunic.humanecology.frontdesk.worker.intro 148178
 
3.1%
tunic.historicalsociety.frontdesk.archivist_glasses.confrontation 130152
 
2.7%
Other values (116) 2751369
57.9%

Most occurring characters

ValueCountFrequency (%)
i 19015742
 
9.2%
t 18420171
 
8.9%
. 17769877
 
8.6%
c 16945595
 
8.2%
e 16098095
 
7.8%
o 14843675
 
7.2%
r 14259649
 
6.9%
s 13241106
 
6.4%
n 12984271
 
6.3%
a 10336242
 
5.0%
Other values (23) 53101821
25.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 186478229
90.1%
Other Punctuation 17769877
 
8.6%
Connector Punctuation 2236815
 
1.1%
Decimal Number 531323
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 19015742
10.2%
t 18420171
9.9%
c 16945595
9.1%
e 16098095
8.6%
o 14843675
 
8.0%
r 14259649
 
7.6%
s 13241106
 
7.1%
n 12984271
 
7.0%
a 10336242
 
5.5%
l 10226959
 
5.5%
Other values (15) 40106724
21.5%
Decimal Number
ValueCountFrequency (%)
0 268671
50.6%
2 119829
22.6%
3 105880
 
19.9%
1 31141
 
5.9%
5 5640
 
1.1%
4 162
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 17769877
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2236815
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 186478229
90.1%
Common 20538015
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 19015742
10.2%
t 18420171
9.9%
c 16945595
9.1%
e 16098095
8.6%
o 14843675
 
8.0%
r 14259649
 
7.6%
s 13241106
 
7.1%
n 12984271
 
7.0%
a 10336242
 
5.5%
l 10226959
 
5.5%
Other values (15) 40106724
21.5%
Common
ValueCountFrequency (%)
. 17769877
86.5%
_ 2236815
 
10.9%
0 268671
 
1.3%
2 119829
 
0.6%
3 105880
 
0.5%
1 31141
 
0.2%
5 5640
 
< 0.1%
4 162
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 207016244
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 19015742
 
9.2%
t 18420171
 
8.9%
. 17769877
 
8.6%
c 16945595
 
8.2%
e 16098095
 
7.8%
o 14843675
 
7.2%
r 14259649
 
6.9%
s 13241106
 
6.4%
n 12984271
 
6.3%
a 10336242
 
5.0%
Other values (23) 53101821
25.7%

level_group
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size627.8 MiB
13-22
6666030 
5-12
4382084 
0-4
1971680 

Length

Max length5
Median length5
Mean length4.3605549
Min length3

Characters and Unicode

Total characters56773526
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0-4
2nd row0-4
3rd row0-4
4th row0-4
5th row0-4

Common Values

ValueCountFrequency (%)
13-22 6666030
51.2%
5-12 4382084
33.7%
0-4 1971680
 
15.1%

Length

2023-03-27T04:48:46.926648image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-27T04:48:47.011889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
13-22 6666030
51.2%
5-12 4382084
33.7%
0-4 1971680
 
15.1%

Most occurring characters

ValueCountFrequency (%)
2 17714144
31.2%
- 13019794
22.9%
1 11048114
19.5%
3 6666030
 
11.7%
5 4382084
 
7.7%
0 1971680
 
3.5%
4 1971680
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43753732
77.1%
Dash Punctuation 13019794
 
22.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 17714144
40.5%
1 11048114
25.3%
3 6666030
 
15.2%
5 4382084
 
10.0%
0 1971680
 
4.5%
4 1971680
 
4.5%
Dash Punctuation
ValueCountFrequency (%)
- 13019794
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 56773526
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 17714144
31.2%
- 13019794
22.9%
1 11048114
19.5%
3 6666030
 
11.7%
5 4382084
 
7.7%
0 1971680
 
3.5%
4 1971680
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56773526
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 17714144
31.2%
- 13019794
22.9%
1 11048114
19.5%
3 6666030
 
11.7%
5 4382084
 
7.7%
0 1971680
 
3.5%
4 1971680
 
3.5%

Interactions

2023-03-27T04:47:31.095867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:44:58.156357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:45:20.841544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:45:42.734812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:46:04.097542image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:46:26.241133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:46:47.741226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:47:09.334903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:47:33.856901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:45:01.047941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:45:23.706328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:45:45.574557image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:46:06.915937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:46:28.998443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:46:50.480098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:47:12.139360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:47:36.614319image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:45:03.991006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:45:26.649749image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:45:48.225219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:46:09.678591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:46:31.726212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:46:53.192527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:47:14.884130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:47:39.387021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:45:06.973856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:45:29.513886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:45:51.022948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:46:12.321724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:46:34.541036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:46:56.163021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:47:17.693618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:47:42.098141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:45:09.720565image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:45:32.163850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:45:53.570378image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:46:14.864804image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:46:37.141081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:46:58.815739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:47:20.364043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:47:44.762856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:45:12.462479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:45:34.732339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:45:56.126971image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:46:18.006330image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:46:39.784266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:47:01.388312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:47:23.028758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:47:47.468101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:45:15.241174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:45:37.288362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:45:58.675025image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:46:20.676818image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:46:42.368718image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:47:03.972739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:47:25.613735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:47:50.117877image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:45:17.990809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:45:39.886817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:46:01.248328image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:46:23.390308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:46:45.016053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:47:06.577296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-27T04:47:28.327573image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-03-27T04:48:47.164968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
session_iddf_indexelapsed_timelevelroom_coor_xroom_coor_yscreen_coor_xscreen_coor_yevent_namenameroom_fqidlevel_group
session_id1.000-0.010-0.018-0.0010.001-0.0060.0060.0090.0080.0030.0060.006
df_index-0.0101.0000.8270.906-0.001-0.1640.0360.0420.0250.0170.0320.066
elapsed_time-0.0180.8271.0000.811-0.023-0.1530.0230.0400.0660.0270.2760.719
level-0.0010.9060.8111.0000.011-0.1590.0400.0270.2230.1040.6620.988
room_coor_x0.001-0.001-0.0230.0111.0000.0870.726-0.1490.1560.1540.3080.196
room_coor_y-0.006-0.164-0.153-0.1590.0871.0000.070-0.8740.1880.3280.3430.240
screen_coor_x0.0060.0360.0230.0400.7260.0701.000-0.0880.1780.2540.1490.102
screen_coor_y0.0090.0420.0400.027-0.149-0.874-0.0881.0000.2150.4040.1590.082
event_name0.0080.0250.0660.2230.1560.1880.1780.2151.0000.5790.3040.199
name0.0030.0170.0270.1040.1540.3280.2540.4040.5791.0000.1190.057
room_fqid0.0060.0320.2760.6620.3080.3430.1490.1590.3040.1191.0000.753
level_group0.0060.0660.7190.9880.1960.2400.1020.0820.1990.0570.7531.000

Missing values

2023-03-27T04:47:50.448889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-27T04:47:58.959234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-27T04:48:27.209654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

session_iddf_indexelapsed_timeevent_namenamelevelroom_coor_xroom_coor_yscreen_coor_xscreen_coor_yfqidroom_fqidtext_fqidlevel_group
02009031243127320000cutscene_clickbasic0-413.991394-159.314682380.0494.0introtunic.historicalsociety.closettunic.historicalsociety.closet.intro0-4
12009031243127320011323person_clickbasic0-413.991394-159.314682380.0494.0grampstunic.historicalsociety.closettunic.historicalsociety.closet.gramps.intro_0_cs_00-4
2200903124312732002831person_clickbasic0-413.991394-159.314682380.0494.0grampstunic.historicalsociety.closettunic.historicalsociety.closet.gramps.intro_0_cs_00-4
32009031243127320031147person_clickbasic0-413.991394-159.314682380.0494.0grampstunic.historicalsociety.closettunic.historicalsociety.closet.gramps.intro_0_cs_00-4
42009031243127320041863person_clickbasic0-412.991394-159.314682381.0494.0grampstunic.historicalsociety.closettunic.historicalsociety.closet.gramps.intro_0_cs_00-4
52009031243127320053423person_clickbasic0-412.991394-157.314682381.0492.0grampstunic.historicalsociety.closettunic.historicalsociety.closet.gramps.intro_0_cs_00-4
62009031243127320065197person_clickbasic0478.485077-199.971680593.0485.0teddytunic.historicalsociety.closettunic.historicalsociety.closet.teddy.intro_0_cs_00-4
72009031243127320076180person_clickbasic0503.355133-168.619919609.0453.0teddytunic.historicalsociety.closettunic.historicalsociety.closet.teddy.intro_0_cs_00-4
82009031243127320087014person_clickbasic0510.733429-157.720642615.0442.0teddytunic.historicalsociety.closettunic.historicalsociety.closet.teddy.intro_0_cs_00-4
92009031243127320097946person_clickbasic0512.048035-153.743637616.0438.0teddytunic.historicalsociety.closettunic.historicalsociety.closet.teddy.intro_0_cs_00-4
session_iddf_indexelapsed_timeevent_namenamelevelroom_coor_xroom_coor_yscreen_coor_xscreen_coor_yfqidroom_fqidtext_fqidlevel_group
131742012210022114501465615953691298navigate_clickundefined2234.820297-335.397980469.0648.0toentrytunic.historicalsociety.stacksNaN13-22
131742022210022114501465615963691298navigate_clickundefined22886.331848-6.926569878.0329.0tomaptunic.historicalsociety.entryNaN13-22
131742032210022114501465615973691298map_hoverbasic22NaNNaNNaNNaNtomaptunic.historicalsociety.entryNaN13-22
131742042210022114501465615983691298map_hoverbasic22NaNNaNNaNNaNtunic.drycleanertunic.historicalsociety.entryNaN13-22
131742052210022114501465615993691298map_clickundefined22470.274750-20.240889455.0342.0tunic.capitol_2tunic.historicalsociety.entryNaN13-22
131742062210022114501465616003691298navigate_clickundefined22343.88729936.701027483.0273.0NaNtunic.capitol_2.hallNaN13-22
131742072210022114501465616013691298navigate_clickundefined22332.696075141.493179545.0221.0chap4_finale_ctunic.capitol_2.hallNaN13-22
131742082210022114501465616023691298navigate_clickundefined22369.912872140.569199611.0217.0NaNtunic.capitol_2.hallNaN13-22
131742092210022114501465616033691298navigate_clickundefined22252.299652123.805893526.0232.0chap4_finale_ctunic.capitol_2.hallNaN13-22
131742102210022114501465616043691298checkpointbasic22NaNNaNNaNNaNchap4_finale_ctunic.capitol_2.hallNaN13-22